Furthermore, the security of controllers formulated by stochastic processes such as a Markov decision process can also be considered.
An example of this type of controller is reinforcement learning, which is an application of encrypted control [1]}.
In this case, the state of the environment randomly moves in each time step, and an action, which is an input for the environment, is determined by a policy that maximizes the expected cumulative reward.
An attacker for an agent of reinforcement learning may be interested in the policy rather than the controller parameter.
Further research on provably secure encrypted control involves considering such stochastic controllers.
